KAUST is launching the "Dear AI" campaign and hackathon to address gender bias and under-representation of women and Saudi/Arab people in AI, after finding AI image tools return only 1% women for prompts like "imagine entrepreneur." The campaign calls for accurate representation in AI datasets from Saudi Arabia and beyond. KAUST notes that 47% of graduates in their AI academy are women. Why it matters: This campaign highlights the need for more inclusive AI training data and addresses gender imbalances in STEM fields in Saudi Arabia.
NYU and NYU Abu Dhabi researchers are working on user-centric gender rewriting in NLP, especially for Arabic. They are building an Arabic Parallel Gender Corpus and developing models for gender rewriting tasks. The work aims to address representational harms caused by NLP systems that don't account for user preferences regarding grammatical gender. Why it matters: This research promotes fairness and inclusivity in Arabic NLP by enabling systems to generate gender-specific outputs based on user preferences, mitigating biases present in training data.
KAUST hosted a regional Women in Data Science (WiDS) conference, part of a global event held at over 100 regional institutions led by Stanford University. The KAUST event featured exclusively female speakers and aimed to highlight data science research and applications. KAUST is launching a 'Women in Data Sciences and Technology' initiative to support women's education and careers in the field. Why it matters: This initiative can help address the underrepresentation of women in data science in Saudi Arabia and the broader region.
The study analyzes over 1,000 images generated by ImageFX, DALL-E V3, and Grok for 56 Saudi professions, finding significant gender imbalances and cultural inaccuracies. DALL-E V3 exhibited the strongest gender stereotyping, with 96% male depictions, particularly in leadership and technical roles. The research underscores the need for diverse training data and culturally sensitive evaluation to ensure equitable AI outputs that accurately reflect Saudi Arabia's labor market and culture.
A panel discussion on women in biology was held as part of the 2016 Fall Enrichment Program at KAUST. Jasmeen Merzaban, Ashwag Abdullah Albukhari, Bettina Berger and Peiying Hong were the speakers. The event featured successful female scientists sharing their experiences. Why it matters: Showcases KAUST's commitment to promoting women in STEM fields and providing a platform for their voices.
MBZUAI researchers found that only 5.7% of music in existing datasets used to train generative music systems comes from non-Western genres. They discovered that 94% of the music represented Western music, while Africa, the Middle East, and South Asia accounted for only 0.3%, 0.4%, and 0.9% respectively. The team also tested whether parameter-efficient fine-tuning with adapters could improve generative music systems on underrepresented styles, presenting their findings at NAACL. Why it matters: This research highlights the critical need for more diverse datasets in AI music generation to better serve global musical traditions and audiences.
A 2016 KAUST Winter Enrichment Program seminar, "Women in Science and Engineering," convened female scientists from KAUST and abroad. Panelists like Jasmeen Merzaban and Charlotte Hauser shared their career experiences and addressed challenges faced by women in STEM. They noted that women constitute 60% of higher education graduates in Saudi Arabia and will be vital to the Kingdom's knowledge economy. Why it matters: The event highlights the increasing role of women in Saudi Arabia's STEM fields and KAUST's commitment to supporting female scientists.